- **Refactoring PDF loaders step 1**: "community: Refactoring PDF
loaders to standardize approaches"
- **Description:** Declare CloudBlobLoader in __init__.py. file_path is
Union[str, PurePath] anywhere
- **Twitter handle:** pprados
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once.
This specific part focuses to prepare the update of all parsers.
For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
@eyurtsev it's the start of a PR series.
community: optimize DataFrame document loader
**Description:**
Simplify the `lazy_load` method in the DataFrame document loader by
combining text extraction and metadata cleanup into a single operation.
This makes the code more concise while maintaining the same
functionality.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** N/A
- **Description:** The aload function, contrary to its name, is not an
asynchronous function, so it cannot work concurrently with other
asynchronous functions.
- **Issue:** #28336
- **Test: **: Done
- **Docs: **
[here](e0a95e5646/docs/docs/integrations/document_loaders/web_base.ipynb (L201))
- **Lint: ** All checks passed
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
(This PR has contributions from @khushiDesai, @ashvini8, and
@ssumaiyaahmed).
This PR addresses **Issue #11229** which addresses the need for SQL
support in document parsing. This is integrated into the generic
TreeSitter parsing library, allowing LangChain users to easily load
codebases in SQL into smaller, manageable "documents."
This pull request adds a new ```SQLSegmenter``` class, which provides
the SQL integration.
## Issue
**Issue #11229**: Add support for a variety of languages to
LanguageParser
## Testing
We created a file ```test_sql.py``` with several tests to ensure the
```SQLSegmenter``` is functional. Below are the tests we added:
- ```def test_is_valid```: Checks SQL validity.
- ```def test_extract_functions_classes```: Extracts individual SQL
statements.
- ```def test_simplify_code```: Simplifies SQL code with comments.
---------
Co-authored-by: Syeda Sumaiya Ahmed <114104419+ssumaiyaahmed@users.noreply.github.com>
Co-authored-by: ashvini hunagund <97271381+ashvini8@users.noreply.github.com>
Co-authored-by: Khushi Desai <khushi.desai@advantawitty.com>
Co-authored-by: Khushi Desai <59741309+khushiDesai@users.noreply.github.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
## Description
This pull request introduces the `DocumentLoaderAsParser` class, which
acts as an adapter to transform document loaders into parsers within the
LangChain framework. The class enables document loaders that accept a
`file_path` parameter to be utilized as blob parsers. This is
particularly useful for integrating various document loading
capabilities seamlessly into the LangChain ecosystem.
When merged in together with PR
https://github.com/langchain-ai/langchain/pull/27716 It opens options
for `SharePointLoader` / `OneDriveLoader` to process any filetype that
has a document loader.
### Features
- **Flexible Parsing**: The `DocumentLoaderAsParser` class can adapt any
document loader that meets the criteria of accepting a `file_path`
argument, allowing for lazy parsing of documents.
- **Compatibility**: The class has been designed to work with various
document loaders, making it versatile for different use cases.
### Usage Example
To use the `DocumentLoaderAsParser`, you would initialize it with a
suitable document loader class and any required parameters. Here’s an
example of how to do this with the `UnstructuredExcelLoader`:
```python
from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers.documentloader_adapter import DocumentLoaderAsParser
from langchain_community.document_loaders.excel import UnstructuredExcelLoader
# Initialize the parser adapter with UnstructuredExcelLoader
xlsx_parser = DocumentLoaderAsParser(UnstructuredExcelLoader, mode="paged")
# Use parser, for ex. pass it to MimeTypeBasedParser
MimeTypeBasedParser(
handlers={
"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": xlsx_parser
}
)
```
- **Dependencies:** None
- **Twitter handle:** @martintriska1
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** One-Bit Images was raising error which has been fixed
in this PR for `PDFPlumberParser`
- **Issue:** #28480
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** I am working to address a similar issue to the one
mentioned in https://github.com/langchain-ai/langchain/pull/19499.
Specifically, there is a problem with the Webbase loader used in
open-webui, where it fails to load the proxy configuration. This PR aims
to resolve that issue.
<!--If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.-->
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description**: Some confluence instances don't support personal access
token, then cookie is a convenient way to authenticate. This PR adds
support for Confluence cookies.
**Twitter handle**: soulmachine
**Description:**
- Add _concatenate_rich_text method to combine all elements in rich text
arrays
- Update load_page method to use _concatenate_rich_text for rich text
properties
- Ensure all text content is captured, including inline code and
formatted text
- Add unit tests to verify correct handling of multi-element rich text
This fix prevents truncation of content after backticks or other
formatting elements.
**Issue:**
Using Notion DB Loader, the text for `richtext` and `title` is truncated
after 1st element was loaded as Notion Loader only read the first
element.
**Dependencies:** any dependencies required for this change
None.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
**PR title**: "community: fix PDF Filter Type Error"
- **Description:** fix PDF Filter Type Error"
- **Issue:** the issue #27153 it fixes,
- **Dependencies:** no
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
JSONparse, in _validate_metadata_func(), checks the consistency of the
_metadata_func() function. To do this, it invokes it and makes sure it
receives a dictionary in response. However, during the call, it does not
respect future calls, as shown on line 100. This generates errors if,
for example, the function is like this:
```python
def generate_metadata(json_node:Dict[str,Any],kwargs:Dict[str,Any]) -> Dict[str,Any]:
return {
"source": url,
"row": kwargs['seq_num'],
"question":json_node.get("question"),
}
loader = JSONLoader(
file_path=file_path,
content_key="answer",
jq_schema='.[]',
metadata_func=generate_metadata,
text_content=False)
```
To avoid this, the verification must comply with the specifications.
This patch does just that.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## What are we doing in this PR
We're adding `modified_since` optional argument to `O365BaseLoader`.
When set, O365 loader will only load documents newer than
`modified_since` datetime.
## Why?
OneDrives / Sharepoints can contain large number of documents. Current
approach is to download and parse all files and let indexer to deal with
duplicates. This can be prohibitively time-consuming. Especially when
using OCR-based parser like
[zerox](fa06188834/libs/community/langchain_community/document_loaders/pdf.py (L948)).
This argument allows to skip documents that are older than known time of
indexing.
_Q: What if a file was modfied during last indexing process?
A: Users can set the `modified_since` conservatively and indexer will
still take care of duplicates._
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR fixes JSONLoader._get_text not converting objects to json string
correctly.
If an object is serializable and is not a dict, JSONLoader will use
python built-in str() method to convert it to string. This may cause
object converted to strings not following json standard. For example, a
list will be converted to string with single quotes, and if json.loads
try to load this string, it will cause error.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** `requests_kwargs` is not being passed to `_fetch`
which is fetching pages asynchronously. In this PR, making sure that we
are passing `requests_kwargs` to `_fetch` just like `_scrape`.
- **Issue:** #28634
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**:
This PR modifies the doc_intelligence.py parser in the community package
to include all metadata returned by the Azure Doc Intelligence API in
the Document object. Previously, only the parsed content (markdown) was
retained, while other important metadata such as bounding boxes (bboxes)
for images and tables was discarded. These image bboxes are crucial for
supporting use cases like multi-modal RAG workflows when using Azure Doc
Intelligence.
The change ensures that all information returned by the Azure Doc
Intelligence API is preserved by setting the metadata attribute of the
Document object to the entire result returned by the API, rather than an
empty dictionary. This extends the parser's utility for complex use
cases without breaking existing functionality.
**Issue**:
This change does not address a specific issue number, but it resolves a
critical limitation in supporting multimodal workflows when using the
LangChain wrapper for the Azure API.
**Dependencies**:
No additional dependencies are required for this change.
---------
Co-authored-by: jmohren <johannes.mohren@aol.de>
# What problem are we fixing?
Currently documents loaded using `O365BaseLoader` fetch source from
`file.web_url` (where `file` is `<class 'O365.drive.File'>`). This works
well for `.pdf` documents. Unfortunately office documents (`.xlsx`,
`.docx` ...) pass their `web_url` in following format:
`https://sharepoint_address/sites/path/to/library/root/Doc.aspx?sourcedoc=%XXXXXXXX-1111-1111-XXXX-XXXXXXXXXX%7D&file=filename.xlsx&action=default&mobileredirect=true`
This obfuscates the path to the file. This PR utilizes the parrent
folder's path and file name to reconstruct the actual location of the
file. Knowing the file's location can be crucial for some RAG
applications (path to the file can carry information we don't want to
loose).
@vbarda Could you please look at this one? I'm @-mentioning you since
we've already closed some PRs together :-)
Co-authored-by: Erick Friis <erick@langchain.dev>
## **Description:**
Enable `ConfluenceLoader` to include labels with `include_labels` option
(`false` by default for backward compatibility). and the labels are set
to `metadata` in the `Document`. e.g. `{"labels": ["l1", "l2"]}`
## Notes
Confluence API supports to get labels by providing `metadata.labels` to
`expand` query parameter
All of the following functions support `expand` in the same way:
- confluence.get_page_by_id
- confluence.get_all_pages_by_label
- confluence.get_all_pages_from_space
- cql (internally using
[/api/content/search](https://developer.atlassian.com/cloud/confluence/rest/v1/api-group-content/#api-wiki-rest-api-content-search-get))
## **Issue:**
No issue related to this PR.
## **Dependencies:**
No changes.
## **Twitter handle:**
[@gymnstcs](https://x.com/gymnstcs)
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- [x] **PR title**: "community: add Needle retriever and document loader
integration"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** This PR adds a new integration for Needle, which
includes:
- **NeedleRetriever**: A retriever for fetching documents from Needle
collections.
- **NeedleLoader**: A document loader for managing and loading documents
into Needle collections.
- Example notebooks demonstrating usage have been added in:
- `docs/docs/integrations/retrievers/needle.ipynb`
- `docs/docs/integrations/document_loaders/needle.ipynb`.
- **Dependencies:** The `needle-python` package is required as an
external dependency for accessing Needle's API. It has been added to the
extended testing dependencies list.
- **Twitter handle:** Feel free to mention me if this PR gets announced:
[needlexai](https://x.com/NeedlexAI).
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. Unit tests have been added for both `NeedleRetriever` and
`NeedleLoader` in `libs/community/tests/unit_tests`. These tests mock
API calls to avoid relying on network access.
2. Example notebooks have been added to `docs/docs/integrations/`,
showcasing both retriever and loader functionality.
- [x] **Lint and test**: Run `make format`, `make lint`, and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
- `make format`: Passed
- `make lint`: Passed
- `make test`: Passed (requires `needle-python` to be installed locally;
this package is not added to LangChain dependencies).
Additional guidelines:
- [x] Optional dependencies are imported only within functions.
- [x] No dependencies have been added to pyproject.toml files except for
those required for unit tests.
- [x] The PR does not touch more than one package.
- [x] Changes are fully backwards compatible.
- [x] Community additions are not re-imported into LangChain core.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** This PR adds functionality to pass in in-memory bytes
as a source to `AzureAIDocumentIntelligenceLoader`.
- **Issue:** I needed the functionality, so I added it.
- **Dependencies:** NA
- **Twitter handle:** @akseljoonas if this is a big enough change :)
---------
Co-authored-by: Aksel Joonas Reedi <aksel@klippa.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
# OCR-based PDF loader
This implements [Zerox](https://github.com/getomni-ai/zerox) PDF
document loader.
Zerox utilizes simple but very powerful (even though slower and more
costly) approach to parsing PDF documents: it converts PDF to series of
images and passes it to a vision model requesting the contents in
markdown.
It is especially suitable for complex PDFs that are not parsed well by
other alternatives.
## Example use:
```python
from langchain_community.document_loaders.pdf import ZeroxPDFLoader
os.environ["OPENAI_API_KEY"] = "" ## your-api-key
model = "gpt-4o-mini" ## openai model
pdf_url = "https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf"
loader = ZeroxPDFLoader(file_path=pdf_url, model=model)
docs = loader.load()
```
The Zerox library supports wide range of provides/models. See Zerox
documentation for details.
- **Dependencies:** `zerox`
- **Twitter handle:** @martintriska1
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
## What this PR does?
### Currently `O365BaseLoader` (and consequently both derived loaders)
are limited to `pdf`, `doc`, `docx` files.
- **Solution: here we introduce _handlers_ attribute that allows for
custom handlers to be passed in. This is done in _dict_ form:**
**Example:**
```python
from langchain_community.document_loaders.parsers.documentloader_adapter import DocumentLoaderAsParser
# PR for DocumentLoaderAsParser here: https://github.com/langchain-ai/langchain/pull/27749
from langchain_community.document_loaders.excel import UnstructuredExcelLoader
xlsx_parser = DocumentLoaderAsParser(UnstructuredExcelLoader, mode="paged")
# create dictionary mapping file types to handlers (parsers)
handlers = {
"doc": MsWordParser()
"pdf": PDFMinerParser()
"txt": TextParser()
"xlsx": xlsx_parser
}
loader = SharePointLoader(document_library_id="...",
handlers=handlers # pass handlers to SharePointLoader
)
documents = loader.load()
# works the same in OneDriveLoader
loader = OneDriveLoader(document_library_id="...",
handlers=handlers
)
```
This dictionary is then passed to `MimeTypeBasedParser` same as in the
[current
implementation](5a2cfb49e0/libs/community/langchain_community/document_loaders/parsers/registry.py (L13)).
### Currently `SharePointLoader` and `OneDriveLoader` are separate
loaders that both inherit from `O365BaseLoader`
However both of these implement the same functionality. The only
differences are:
- `SharePointLoader` requires argument `document_library_id` whereas
`OneDriveLoader` requires `drive_id`. These are just different names for
the same thing.
- `SharePointLoader` implements significantly more features.
- **Solution: `OneDriveLoader` is replaced with an empty shell just
renaming `drive_id` to `document_library_id` and inheriting from
`SharePointLoader`**
**Dependencies:** None
**Twitter handle:** @martintriska1
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
The metadata["source"] value for the web paths was being set to
temporary path (/tmp).
Fixed it by creating a new variable self.original_file_path, which will
store the original path.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description**
This PR introduces the proxies parameter to the RecursiveUrlLoader
class, allowing the user to specify proxy servers for requests. This
update enables crawling through proxy servers, providing enhanced
flexibility for network configurations.
The key changes include:
1.Added an optional proxies parameter to the constructor (__init__).
2.Updated the documentation to explain the proxies parameter usage with
an example.
3.Modified the _get_child_links_recursive method to pass the proxies
parameter to the requests.get function.
**Sample Usage**
```python
from bs4 import BeautifulSoup as Soup
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader
proxies = {
"http": "http://localhost:1080",
"https": "http://localhost:1080",
}
url = "https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel"
loader = RecursiveUrlLoader(
url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text,proxies=proxies
)
docs = loader.load()
```
---------
Co-authored-by: root <root@thb>
**Description**:
This PR add support of clob/blob data type for oracle document loader,
clob/blob can only be read by oracledb package when connection is open,
so reformat code to process data before connection closes.
**Dependencies**:
oracledb package same as before. pip install oracledb
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR updates the Firecrawl Document Loader to use the recently
released V1 API of Firecrawl.
**Key Updates:**
**Firecrawl V1 Integration:** Updated the document loader to leverage
the new Firecrawl V1 API for improved performance, reliability, and
developer experience.
**Map Functionality Added:** Introduced the map mode for more flexible
document loading options.
These updates enhance the integration and provide access to the latest
features of Firecrawl.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** This pull request addresses the validation error in
`SettingsConfigDict` due to extra fields in the `.env` file. The issue
is prevalent across multiple Langchain modules. This fix ensures that
extra fields in the `.env` file are ignored, preventing validation
errors.
**Changes include:**
- Applied fixes to modules using `SettingsConfigDict`.
- **Issue:** NA, similar
https://github.com/langchain-ai/langchain/issues/26850
- **Dependencies:** NA
- **Description:** The flag is named `anonymize_snippets`. When set to
true, the Pebblo server will anonymize snippets by redacting all
personally identifiable information (PII) from the snippets going into
VectorDB and the generated reports
- **Issue:** NA
- **Dependencies:** NA
- **docs**: Updated
- **Description:** Added PebbloTextLoader for loading text in
PebbloSafeLoader.
- Since PebbloSafeLoader wraps document loaders, this new loader enables
direct loading of text into Documents using PebbloSafeLoader.
- **Issue:** NA
- **Dependencies:** NA
- [x] **Tests**: Added/Updated tests
Page content sometimes is empty when PyMuPDF can not find text on pages.
For example, this can happen when the text of the PDF is not copyable
"by hand". Then an OCR solution is need - which is not integrated here.
This warning should accurately warn the user that some pages are lost
during this process.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Fixes#26212: replaced the raw string with backslashes. Alternative:
raw-stringif the full docstring.
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
### Description:
This pull request significantly enhances the MongodbLoader class in the
LangChain community package by adding robust metadata customization and
improved field extraction capabilities. The updated class now allows
users to specify additional metadata fields through the metadata_names
parameter, enabling the extraction of both top-level and deeply nested
document attributes as metadata. This flexibility is crucial for users
who need to include detailed contextual information without altering the
database schema.
Moreover, the include_db_collection_in_metadata flag offers optional
inclusion of database and collection names in the metadata, allowing for
even greater customization depending on the user's needs.
The loader's field extraction logic has been refined to handle missing
or nested fields more gracefully. It now employs a safe access mechanism
that avoids the KeyError previously encountered when a specified nested
field was absent in a document. This update ensures that the loader can
handle diverse and complex data structures without failure, making it
more resilient and user-friendly.
### Issue:
This pull request addresses a critical issue where the MongodbLoader
class in the LangChain community package could throw a KeyError when
attempting to access nested fields that may not exist in some documents.
The previous implementation did not handle the absence of specified
nested fields gracefully, leading to runtime errors and interruptions in
data processing workflows.
This enhancement ensures robust error handling by safely accessing
nested document fields, using default values for missing data, thus
preventing KeyError and ensuring smoother operation across various data
structures in MongoDB. This improvement is crucial for users working
with diverse and complex data sets, ensuring the loader can adapt to
documents with varying structures without failing.
### Dependencies:
Requires motor for asynchronous MongoDB interaction.
### Twitter handle:
N/A
### Add tests and docs
Tests: Unit tests have been added to verify that the metadata inclusion
toggle works as expected and that the field extraction correctly handles
nested fields.
Docs: An example notebook demonstrating the use of the enhanced
MongodbLoader is included in the docs/docs/integrations directory. This
notebook includes setup instructions, example usage, and outputs.
(Here is the notebook link : [colab
link](https://colab.research.google.com/drive/1tp7nyUnzZa3dxEFF4Kc3KS7ACuNF6jzH?usp=sharing))
Lint and test
Before submitting, I ran make format, make lint, and make test as per
the contribution guidelines. All tests pass, and the code style adheres
to the LangChain standards.
```python
import unittest
from unittest.mock import patch, MagicMock
import asyncio
from langchain_community.document_loaders.mongodb import MongodbLoader
class TestMongodbLoader(unittest.TestCase):
def setUp(self):
"""Setup the MongodbLoader test environment by mocking the motor client
and database collection interactions."""
# Mocking the AsyncIOMotorClient
self.mock_client = MagicMock()
self.mock_db = MagicMock()
self.mock_collection = MagicMock()
self.mock_client.get_database.return_value = self.mock_db
self.mock_db.get_collection.return_value = self.mock_collection
# Initialize the MongodbLoader with test data
self.loader = MongodbLoader(
connection_string="mongodb://localhost:27017",
db_name="testdb",
collection_name="testcol"
)
@patch('langchain_community.document_loaders.mongodb.AsyncIOMotorClient', return_value=MagicMock())
def test_constructor(self, mock_motor_client):
"""Test if the constructor properly initializes with the correct database and collection names."""
loader = MongodbLoader(
connection_string="mongodb://localhost:27017",
db_name="testdb",
collection_name="testcol"
)
self.assertEqual(loader.db_name, "testdb")
self.assertEqual(loader.collection_name, "testcol")
def test_aload(self):
"""Test the aload method to ensure it correctly queries and processes documents."""
# Setup mock data and responses for the database operations
self.mock_collection.count_documents.return_value = asyncio.Future()
self.mock_collection.count_documents.return_value.set_result(1)
self.mock_collection.find.return_value = [
{"_id": "1", "content": "Test document content"}
]
# Run the aload method and check responses
loop = asyncio.get_event_loop()
results = loop.run_until_complete(self.loader.aload())
self.assertEqual(len(results), 1)
self.assertEqual(results[0].page_content, "Test document content")
def test_construct_projection(self):
"""Verify that the projection dictionary is constructed correctly based on field names."""
self.loader.field_names = ['content', 'author']
self.loader.metadata_names = ['timestamp']
expected_projection = {'content': 1, 'author': 1, 'timestamp': 1}
projection = self.loader._construct_projection()
self.assertEqual(projection, expected_projection)
if __name__ == '__main__':
unittest.main()
```
### Additional Example for Documentation
Sample Data:
```json
[
{
"_id": "1",
"title": "Artificial Intelligence in Medicine",
"content": "AI is transforming the medical industry by providing personalized medicine solutions.",
"author": {
"name": "John Doe",
"email": "john.doe@example.com"
},
"tags": ["AI", "Healthcare", "Innovation"]
},
{
"_id": "2",
"title": "Data Science in Sports",
"content": "Data science provides insights into player performance and strategic planning in sports.",
"author": {
"name": "Jane Smith",
"email": "jane.smith@example.com"
},
"tags": ["Data Science", "Sports", "Analytics"]
}
]
```
Example Code:
```python
loader = MongodbLoader(
connection_string="mongodb://localhost:27017",
db_name="example_db",
collection_name="articles",
filter_criteria={"tags": "AI"},
field_names=["title", "content"],
metadata_names=["author.name", "author.email"],
include_db_collection_in_metadata=True
)
documents = loader.load()
for doc in documents:
print("Page Content:", doc.page_content)
print("Metadata:", doc.metadata)
```
Expected Output:
```
Page Content: Artificial Intelligence in Medicine AI is transforming the medical industry by providing personalized medicine solutions.
Metadata: {'author_name': 'John Doe', 'author_email': 'john.doe@example.com', 'database': 'example_db', 'collection': 'articles'}
```
Thank you.
---
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:**
Adding a new option to the CSVLoader that allows us to implicitly
specify the columns that are used for generating the Document content.
Currently these are implicitly set as "all fields not part of the
metadata_columns".
In some cases however it is useful to have a field both as a metadata
and as part of the document content.
- **Description:** Added `ref` query parameter so data is not loaded
only from the default branch but any branch passed
---------
Co-authored-by: Osama Mehdi <mehdi@hm.edu>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Added langchain version while calling discover API
during both ingestion and retrieval
- **Issue:** NA
- **Dependencies:** NA
- **Tests:** NA
- **Docs** NA
---------
Co-authored-by: dristy.cd <dristy@clouddefense.io>
- **Description:** Updating source path and file path in Pebblo safe
loader for SharePoint apps during loading
- **Issue:** NA
- **Dependencies:** NA
- **Tests:** NA
- **Docs** NA
---------
Co-authored-by: dristy.cd <dristy@clouddefense.io>
This PR adds tiny improvements to the `GithubFileLoader` document loader
and its code sample, addressing the following issues:
1. Currently, the `file_extension` argument of `GithubFileLoader` does
not change its behavior at all.
1. The `GithubFileLoader` sample code in
`docs/docs/integrations/document_loaders/github.ipynb` does not work as
it stands.
The respective solutions I propose are the following:
1. Remove `file_extension` argument from `GithubFileLoader`.
1. Specify the branch as `master` (not the default `main`) and rename
`documents` as `document`.
---------
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
**Refactor PebbloSafeLoader**
- Created `APIWrapper` and moved API logic into it.
- Moved helper functions to the utility file.
- Created smaller functions and methods for better readability.
- Properly read environment variables.
- Removed unused code.
**Issue:** NA
**Dependencies:** NA
**tests**: Updated
